As communication technologies have improved, businesses and individuals have desired greater functionality in their communication networks. As a nonlimiting example, many businesses have created call center infrastructures, in which a customer or other user can call to receive information related to the business. As customers call into the call center, the customer may be connected with a customer service representative to provide desired information. Depending on the time of call, the subject matter of the call, and/or other information, the customer may be connected with different customer service representatives. As such, depending on these and/or other factors, the customer may be provided with varying levels of quality with respect to the interaction with a customer service representative. Because most businesses desire to provide the highest possible quality of customer service, many businesses have turned to recording the communication between the customer and the customer service representative. While recording this data has proven beneficial in many cases, many businesses receive call volumes that inhibit the business from reviewing all of the call data received.
As such, many businesses have turned to speech recognition technology to capture the recorded communication data and thereby provide a textual document for review of the communication. While textual documentation of a communication has also proven beneficial, a similar scenario may exist, in that the sheer amount of data may be such that review of the data is impractical. To combat this problem, a number of businesses have also implemented speech analytics technologies to analyze the speech recognized communications. One such technology that has emerged includes large vocabulary continuous speech recognition (LVCSR). LVCSR technologies often convert received audio from the communications into an English translation of the communication in a textual document. From the textual document, analytics may be provided to determine various data related to the communication.
While LVCSR technologies have improved the ability to analyze captured data, LVCSR technology often consumes a large amount of resources in converting the audio data into a textual format and/or analyzing the textual data. As such, phonetic speech to text technologies have also emerged. While phonetic speech to text technologies provide analytic functionality, many of the features that may be provided in an LVCSR type speech to text technology may be unavailable.
Thus, a heretofore unaddressed need exists in the industry to address the aforementioned deficiencies and inadequacies.